The latest generation of artificial neural networks (ANNs) exploits capabilities such as online learning, fast training, high level knowledge representation, online evolution, learning by data and inferring rules.Wearable electronics is also developing rapidly and represents an important enabling technology to deploy physical and practical (noninvasive) devices using AI-based models for early prediction of neurodegenerative diseases and of intelligent prostheses.Here we describe how to apply advanced brain-inspired methods for inference and prediction, the evolving fuzzy neural network (EFuNN) paradigm and the spiking neural network (SNN) paradigm, and the system requirements to develop a wearable electronic prosthesis for functional rehabilitation.Recently, digitization of biomedical processes has accelerated, in no small part due to the use of machine learning techniques which require large amounts of labeled data. This chapter focuses on the prerequisite steps to the training of any algorithm data collection and labeling. In particular, we tackle how data collection can be set up with scalability and security to avoid costly and delaying bottlenecks. Unprecedented amounts of data are now available to companies and academics, but digital tools in the biomedical field encounter a problem of scale, since high-throughput workflows such as high content imaging and sequencing can create several terabytes per day. Consequently data transport, aggregation, and processing is challenging.A second challenge is maintenance of data security. Biomedical data can be personally identifiable, may constitute important trade-secrets, and be expensive to produce. Furthermore, human biomedical data is often immutable, as is the case with genetic information. https://www.selleckchem.com/products/pifithrin-alpha.html These factors make securing this type of data imperative and urgent. Here we address best practices to achieve security, with a focus on practicality and scalability. We also address the challenge of obtaining usable, rich metadata from the collected data, which is a major challenge in the biomedical field because of the use of fragmented and proprietary formats. We detail tools and strategies for extracting metadata from biomedical scientific file formats and how this underutilized metadata plays a key role in creating labeled data for use in the training of neural networks.We have studied the ability of three types of neural networks to predict the closeness of a given protein model to the native structure associated with its sequence. We show that a partial combination of the Levenberg-Marquardt algorithm and the back-propagation algorithm produced the best results, giving the lowest error and largest Pearson correlation coefficient. We also find, as previous studies, that adding associative memory to a neural network improves its performance. Additionally, we find that the hybrid method we propose was the most robust in the sense that other configurations of it experienced less decline in comparison to the other methods. We find that the hybrid networks also undergo more fluctuations on the path to convergence. We propose that these fluctuations allow for better sampling. Overall we find it may be beneficial to treat different parts of a neural network with varied computational approaches during optimization.Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Targeting protein-protein interactions is a challenge and crucial task of the drug discovery process. A good starting point for rational drug design is the identification of hot spots (HS) at protein-protein interfaces, typically conserved residues that contribute most significantly to the binding. In this chapter, we depict point-by-point an in-house pipeline used for HS prediction using only sequence-based features from the well-known SpotOn dataset of soluble proteins (Moreira et al., Sci Rep 78007, 2017), through the implementation of a deep neural network. The presented pipeline is divided into three steps (1) feature extraction, (2) deep learning classification, and (3) model evaluation. We present all the available resources, including code snippets, the main dataset, and the free and open-source modules/packages necessary for full replication of the protocol. The users should be able to develop an HS prediction model with accuracy, precision, recall, and AUROC of 0.96, 0.93, 0.91, and 0.86, respectively.Accurate prediction of the host phenotypes from a microbial sample and identification of the associated microbial markers are important in understanding the impact of the microbiome on the pathogenesis and progression of various diseases within the host. A deep learning tool, PopPhy-CNN, has been developed for the task of predicting host phenotypes using a convolutional neural network (CNN). By representing samples as annotated taxonomic trees and further representing these trees as matrices, PopPhy-CNN utilizes the CNN's innate ability to explore locally similar microbes on the taxonomic tree. Furthermore, PopPhy-CNN can be used to evaluate the importance of each taxon in the prediction of host status. Here, we describe the underlying methodology, architecture, and core utility of PopPhy-CNN. We also demonstrate the use of PopPhy-CNN on a microbial dataset.